
from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import (
							    TF_MODEL_FOR_PRETRAINING_MAPPING,
							    TFMobileBertForMaskedLM,
							    TFMobileBertForMultipleChoice,
							    TFMobileBertForNextSentencePrediction,
							    TFMobileBertForPreTraining,
							    TFMobileBertForQuestionAnswering,
							    TFMobileBertForSequenceClassification,
							    TFMobileBertForTokenClassification,
							    TFMobileBertModel,
							)

@require_tf
class 							_UpperCamelCase					(       _A  ,							_A  ,							unittest.TestCase	):






			'''simple docstring'''

			__UpperCamelCase   :    Union[str, Any]          =			(
			    (
			        TFMobileBertModel,
			        TFMobileBertForMaskedLM,
			        TFMobileBertForNextSentencePrediction,
			        TFMobileBertForPreTraining,
			        TFMobileBertForQuestionAnswering,
			        TFMobileBertForSequenceClassification,
			        TFMobileBertForTokenClassification,
			        TFMobileBertForMultipleChoice,
			    )
			    if is_tf_available()
			    else ()
			)
			__UpperCamelCase   :    List[Any]          =			(
			    {
			        """feature-extraction""": TFMobileBertModel,
			        """fill-mask""": TFMobileBertForMaskedLM,
			        """question-answering""": TFMobileBertForQuestionAnswering,
			        """text-classification""": TFMobileBertForSequenceClassification,
			        """token-classification""": TFMobileBertForTokenClassification,
			        """zero-shot""": TFMobileBertForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			__UpperCamelCase   :    Optional[Any]          =			False
			__UpperCamelCase   :    Optional[int]          =			False

			def    lowerCAmelCase__						(							self			:							List[Any]    ,						snake_case_			:							str    ,						snake_case_			:							List[str]    ,						snake_case_			:							Optional[int]=False   ):
							UpperCamelCase_:				Optional[Any]						=    super()._prepare_for_class(snake_case_    ,						snake_case_    ,						return_labels=snake_case_   )

							if return_labels:
											if model_class in get_values(snake_case_   ):
															UpperCamelCase_:				Optional[Any]						=    tf.zeros(self.model_tester.batch_size    ,						dtype=tf.intaa   )

							return inputs_dict

			class 							_UpperCamelCase					(       _A	):






						'''simple docstring'''

						def __init__(							self			:							List[str]    ,						snake_case_			:							int    ,						snake_case_			:							int=13    ,						snake_case_			:							Optional[int]=7    ,						snake_case_			:							Tuple=True    ,						snake_case_			:							Any=True    ,						snake_case_			:							str=True    ,						snake_case_			:							Tuple=True    ,						snake_case_			:							List[str]=99    ,						snake_case_			:							str=32    ,						snake_case_			:							str=32    ,						snake_case_			:							List[Any]=2    ,						snake_case_			:							Union[str, Any]=4    ,						snake_case_			:							Dict=37    ,						snake_case_			:							Union[str, Any]="gelu"    ,						snake_case_			:							List[Any]=0.1    ,						snake_case_			:							List[Any]=0.1    ,						snake_case_			:							List[str]=512    ,						snake_case_			:							Optional[int]=16    ,						snake_case_			:							Dict=2    ,						snake_case_			:							Any=0.02    ,						snake_case_			:							Any=3    ,						snake_case_			:							Optional[Any]=4    ,						snake_case_			:							int=None    ,						):
										UpperCamelCase_:				Dict						=    parent
										UpperCamelCase_:				Tuple						=    batch_size
										UpperCamelCase_:				Union[str, Any]						=    seq_length
										UpperCamelCase_:				Any						=    is_training
										UpperCamelCase_:				Any						=    use_input_mask
										UpperCamelCase_:				List[str]						=    use_token_type_ids
										UpperCamelCase_:				List[str]						=    use_labels
										UpperCamelCase_:				List[str]						=    vocab_size
										UpperCamelCase_:				str						=    hidden_size
										UpperCamelCase_:				Tuple						=    num_hidden_layers
										UpperCamelCase_:				Dict						=    num_attention_heads
										UpperCamelCase_:				int						=    intermediate_size
										UpperCamelCase_:				Tuple						=    hidden_act
										UpperCamelCase_:				Optional[Any]						=    hidden_dropout_prob
										UpperCamelCase_:				List[str]						=    attention_probs_dropout_prob
										UpperCamelCase_:				Optional[int]						=    max_position_embeddings
										UpperCamelCase_:				int						=    type_vocab_size
										UpperCamelCase_:				Dict						=    type_sequence_label_size
										UpperCamelCase_:				List[str]						=    initializer_range
										UpperCamelCase_:				int						=    num_labels
										UpperCamelCase_:				str						=    num_choices
										UpperCamelCase_:				List[str]						=    scope
										UpperCamelCase_:				List[Any]						=    embedding_size

						def    lowerCAmelCase__						(							self			:							Any   ):
										UpperCamelCase_:				int						=    ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size   )

										UpperCamelCase_:				List[Any]						=    None
										if self.use_input_mask:
														UpperCamelCase_:				Any						=    random_attention_mask([self.batch_size, self.seq_length]   )

										UpperCamelCase_:				Optional[int]						=    None
										if self.use_token_type_ids:
														UpperCamelCase_:				Optional[Any]						=    ids_tensor([self.batch_size, self.seq_length]    ,						self.type_vocab_size   )

										UpperCamelCase_:				List[Any]						=    None
										UpperCamelCase_:				int						=    None
										UpperCamelCase_:				int						=    None
										if self.use_labels:
														UpperCamelCase_:				Optional[int]						=    ids_tensor([self.batch_size]    ,						self.type_sequence_label_size   )
														UpperCamelCase_:				Dict						=    ids_tensor([self.batch_size, self.seq_length]    ,						self.num_labels   )
														UpperCamelCase_:				Any						=    ids_tensor([self.batch_size]    ,						self.num_choices   )

										UpperCamelCase_:				Optional[int]						=    MobileBertConfig(
										    vocab_size=self.vocab_size    ,						hidden_size=self.hidden_size    ,						num_hidden_layers=self.num_hidden_layers    ,						num_attention_heads=self.num_attention_heads    ,						intermediate_size=self.intermediate_size    ,						hidden_act=self.hidden_act    ,						hidden_dropout_prob=self.hidden_dropout_prob    ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,						max_position_embeddings=self.max_position_embeddings    ,						type_vocab_size=self.type_vocab_size    ,						initializer_range=self.initializer_range    ,						embedding_size=self.embedding_size    ,						)

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

						def    lowerCAmelCase__						(							self			:							Union[str, Any]    ,						snake_case_			:							Optional[int]    ,						snake_case_			:							Tuple    ,						snake_case_			:							List[str]    ,						snake_case_			:							Union[str, Any]    ,						snake_case_			:							Tuple    ,						snake_case_			:							List[str]    ,						snake_case_			:							List[Any]   ):
										UpperCamelCase_:				Dict						=    TFMobileBertModel(config=snake_case_   )
										UpperCamelCase_:				int						=    {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
										UpperCamelCase_:				List[str]						=    model(snake_case_   )

										UpperCamelCase_:				Dict						=    [input_ids, input_mask]
										UpperCamelCase_:				int						=    model(snake_case_   )

										UpperCamelCase_:				int						=    model(snake_case_   )

										self.parent.assertEqual(
										    result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)   )
										self.parent.assertEqual(result.pooler_output.shape    ,						(self.batch_size, self.hidden_size)   )

						def    lowerCAmelCase__						(							self			:							int    ,						snake_case_			:							int    ,						snake_case_			:							int    ,						snake_case_			:							Union[str, Any]    ,						snake_case_			:							Tuple    ,						snake_case_			:							Optional[Any]    ,						snake_case_			:							int    ,						snake_case_			:							str   ):
										UpperCamelCase_:				Any						=    TFMobileBertForMaskedLM(config=snake_case_   )
										UpperCamelCase_:				Tuple						=    {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
										UpperCamelCase_:				Optional[int]						=    model(snake_case_   )
										self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.vocab_size)   )

						def    lowerCAmelCase__						(							self			:							Optional[int]    ,						snake_case_			:							Optional[int]    ,						snake_case_			:							List[Any]    ,						snake_case_			:							Tuple    ,						snake_case_			:							str    ,						snake_case_			:							str    ,						snake_case_			:							Optional[Any]    ,						snake_case_			:							List[str]   ):
										UpperCamelCase_:				Union[str, Any]						=    TFMobileBertForNextSentencePrediction(config=snake_case_   )
										UpperCamelCase_:				Optional[int]						=    {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
										UpperCamelCase_:				Union[str, Any]						=    model(snake_case_   )
										self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, 2)   )

						def    lowerCAmelCase__						(							self			:							Any    ,						snake_case_			:							str    ,						snake_case_			:							Dict    ,						snake_case_			:							Any    ,						snake_case_			:							Tuple    ,						snake_case_			:							Any    ,						snake_case_			:							Optional[Any]    ,						snake_case_			:							Tuple   ):
										UpperCamelCase_:				Any						=    TFMobileBertForPreTraining(config=snake_case_   )
										UpperCamelCase_:				List[str]						=    {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
										UpperCamelCase_:				Optional[Any]						=    model(snake_case_   )
										self.parent.assertEqual(
										    result.prediction_logits.shape    ,						(self.batch_size, self.seq_length, self.vocab_size)   )
										self.parent.assertEqual(result.seq_relationship_logits.shape    ,						(self.batch_size, 2)   )

						def    lowerCAmelCase__						(							self			:							Union[str, Any]    ,						snake_case_			:							Dict    ,						snake_case_			:							Dict    ,						snake_case_			:							Optional[int]    ,						snake_case_			:							Any    ,						snake_case_			:							Tuple    ,						snake_case_			:							Union[str, Any]    ,						snake_case_			:							str   ):
										UpperCamelCase_:				Optional[Any]						=    self.num_labels
										UpperCamelCase_:				Any						=    TFMobileBertForSequenceClassification(config=snake_case_   )
										UpperCamelCase_:				Any						=    {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
										UpperCamelCase_:				List[Any]						=    model(snake_case_   )
										self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels)   )

						def    lowerCAmelCase__						(							self			:							int    ,						snake_case_			:							List[Any]    ,						snake_case_			:							Dict    ,						snake_case_			:							Dict    ,						snake_case_			:							Tuple    ,						snake_case_			:							Optional[Any]    ,						snake_case_			:							Union[str, Any]    ,						snake_case_			:							int   ):
										UpperCamelCase_:				Tuple						=    self.num_choices
										UpperCamelCase_:				Optional[Any]						=    TFMobileBertForMultipleChoice(config=snake_case_   )
										UpperCamelCase_:				Union[str, Any]						=    tf.tile(tf.expand_dims(snake_case_    ,						1   )    ,						(1, self.num_choices, 1)   )
										UpperCamelCase_:				int						=    tf.tile(tf.expand_dims(snake_case_    ,						1   )    ,						(1, self.num_choices, 1)   )
										UpperCamelCase_:				Tuple						=    tf.tile(tf.expand_dims(snake_case_    ,						1   )    ,						(1, self.num_choices, 1)   )
										UpperCamelCase_:				Union[str, Any]						=    {
										    """input_ids""": multiple_choice_inputs_ids,
										    """attention_mask""": multiple_choice_input_mask,
										    """token_type_ids""": multiple_choice_token_type_ids,
										}
										UpperCamelCase_:				List[str]						=    model(snake_case_   )
										self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_choices)   )

						def    lowerCAmelCase__						(							self			:							Optional[int]    ,						snake_case_			:							List[Any]    ,						snake_case_			:							List[str]    ,						snake_case_			:							Dict    ,						snake_case_			:							str    ,						snake_case_			:							str    ,						snake_case_			:							List[Any]    ,						snake_case_			:							Any   ):
										UpperCamelCase_:				Optional[Any]						=    self.num_labels
										UpperCamelCase_:				Optional[Any]						=    TFMobileBertForTokenClassification(config=snake_case_   )
										UpperCamelCase_:				Optional[Any]						=    {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
										UpperCamelCase_:				Tuple						=    model(snake_case_   )
										self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.num_labels)   )

						def    lowerCAmelCase__						(							self			:							List[Any]    ,						snake_case_			:							Any    ,						snake_case_			:							Any    ,						snake_case_			:							str    ,						snake_case_			:							str    ,						snake_case_			:							Tuple    ,						snake_case_			:							List[str]    ,						snake_case_			:							Union[str, Any]   ):
										UpperCamelCase_:				Any						=    TFMobileBertForQuestionAnswering(config=snake_case_   )
										UpperCamelCase_:				List[Any]						=    {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
										UpperCamelCase_:				List[str]						=    model(snake_case_   )
										self.parent.assertEqual(result.start_logits.shape    ,						(self.batch_size, self.seq_length)   )
										self.parent.assertEqual(result.end_logits.shape    ,						(self.batch_size, self.seq_length)   )




						def    lowerCAmelCase__						(							self			:							Dict   ):
										UpperCamelCase_:				int						=    self.prepare_config_and_inputs()
										(
										    (
										    UpperCamelCase_
										), (
										    UpperCamelCase_
										), (
										    UpperCamelCase_
										), (
										    UpperCamelCase_
										), (
										    UpperCamelCase_
										), (
										    UpperCamelCase_
										), (
										    UpperCamelCase_
										), 
										):				Optional[int]						=    config_and_inputs
										UpperCamelCase_:				List[str]						=    {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
										return config, inputs_dict




			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				int						=    TFMobileBertModelTest.TFMobileBertModelTester(self   )
							UpperCamelCase_:				List[str]						=    ConfigTester(self    ,						config_class=snake_case_    ,						hidden_size=37   )

			def    lowerCAmelCase__						(							self			:							Optional[int]   ):
							self.config_tester.run_common_tests()

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							UpperCamelCase_:				int						=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_model(*snake_case_   )

			def    lowerCAmelCase__						(							self			:							List[str]   ):
							UpperCamelCase_:				List[Any]						=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case_   )

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Optional[int]						=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case_   )

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							UpperCamelCase_:				List[str]						=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case_   )

			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				Tuple						=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case_   )

			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							UpperCamelCase_:				int						=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case_   )

			def    lowerCAmelCase__						(							self			:							List[str]   ):
							UpperCamelCase_:				Optional[Any]						=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case_   )

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Tuple						=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case_   )




			@slow
			def    lowerCAmelCase__						(							self			:							Optional[int]   ):
							# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							for model_name in ["google/mobilebert-uncased"]:
											UpperCamelCase_:				str						=    TFMobileBertModel.from_pretrained(snake_case_   )
											self.assertIsNotNone(snake_case_   )




@require_tf
class 							_UpperCamelCase					(       unittest.TestCase	):






			'''simple docstring'''

			@slow
			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							UpperCamelCase_:				Optional[int]						=    TFMobileBertForPreTraining.from_pretrained("""google/mobilebert-uncased"""   )
							UpperCamelCase_:				Union[str, Any]						=    tf.constant([[0, 1, 2, 3, 4, 5]]   )
							UpperCamelCase_:				Dict						=    model(snake_case_   )[0]

							UpperCamelCase_:				str						=    [1, 6, 3_0522]
							self.assertEqual(output.shape    ,						snake_case_   )

							UpperCamelCase_:				Optional[Any]						=    tf.constant(
							    [
							        [
							            [-4.591_9547, -9.24_8295, -9.64_5256],
							            [-6.730_6175, -6.44_0284, -6.605_2837],
							            [-7.274_3506, -6.784_7915, -6.02_4673],
							        ]
							    ]   )
							tf.debugging.assert_near(output[:, :3, :3]    ,						snake_case_    ,						atol=1e-4   )








import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Tuple          =			(CMStochasticIterativeScheduler,)
			__UpperCamelCase   :    List[str]          =			10

			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						**snake_case_			:							List[str]   ):
							UpperCamelCase_:				Optional[int]						=    {
							    """num_train_timesteps""": 201,
							    """sigma_min""": 0.002,
							    """sigma_max""": 80.0,
							}

							config.update(**snake_case_   )
							return config

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							UpperCamelCase_:				Tuple						=    10

							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[str]						=    self.scheduler_classes[0](**snake_case_   )

							scheduler.set_timesteps(snake_case_   )

							UpperCamelCase_:				int						=    scheduler.timesteps[0]
							UpperCamelCase_:				str						=    scheduler.timesteps[1]

							UpperCamelCase_:				Optional[int]						=    self.dummy_sample
							UpperCamelCase_:				int						=    0.1 * sample

							UpperCamelCase_:				Optional[Any]						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_   ).prev_sample
							UpperCamelCase_:				Any						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_   ).prev_sample

							self.assertEqual(output_a.shape    ,						sample.shape   )
							self.assertEqual(output_a.shape    ,						output_a.shape   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							for clip_denoised in [True, False]:
											self.check_over_configs(clip_denoised=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Any   ):
							UpperCamelCase_:				Any						=    self.scheduler_classes[0]
							UpperCamelCase_:				List[str]						=    self.get_scheduler_config()
							UpperCamelCase_:				Tuple						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Optional[int]						=    1
							scheduler.set_timesteps(snake_case_   )
							UpperCamelCase_:				Any						=    scheduler.timesteps

							UpperCamelCase_:				int						=    torch.manual_seed(0   )

							UpperCamelCase_:				Any						=    self.dummy_model()
							UpperCamelCase_:				Union[str, Any]						=    self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(snake_case_   ):
											# 1. scale model input
											UpperCamelCase_:				str						=    scheduler.scale_model_input(snake_case_    ,						snake_case_   )

											# 2. predict noise residual
											UpperCamelCase_:				List[Any]						=    model(snake_case_    ,						snake_case_   )

											# 3. predict previous sample x_t-1
											UpperCamelCase_:				str						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						generator=snake_case_   ).prev_sample

											UpperCamelCase_:				Union[str, Any]						=    pred_prev_sample

							UpperCamelCase_:				int						=    torch.sum(torch.abs(snake_case_   )   )
							UpperCamelCase_:				List[Any]						=    torch.mean(torch.abs(snake_case_   )   )

							assert abs(result_sum.item() - 192.7614   ) < 1e-2
							assert abs(result_mean.item() - 0.2510   ) < 1e-3

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Dict						=    self.scheduler_classes[0]
							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[Any]						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				str						=    [106, 0]
							scheduler.set_timesteps(timesteps=snake_case_   )
							UpperCamelCase_:				int						=    scheduler.timesteps

							UpperCamelCase_:				List[Any]						=    torch.manual_seed(0   )

							UpperCamelCase_:				List[Any]						=    self.dummy_model()
							UpperCamelCase_:				int						=    self.dummy_sample_deter * scheduler.init_noise_sigma

							for t in timesteps:
											# 1. scale model input
											UpperCamelCase_:				Optional[int]						=    scheduler.scale_model_input(snake_case_    ,						snake_case_   )

											# 2. predict noise residual
											UpperCamelCase_:				Dict						=    model(snake_case_    ,						snake_case_   )

											# 3. predict previous sample x_t-1
											UpperCamelCase_:				str						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						generator=snake_case_   ).prev_sample

											UpperCamelCase_:				List[str]						=    pred_prev_sample

							UpperCamelCase_:				Union[str, Any]						=    torch.sum(torch.abs(snake_case_   )   )
							UpperCamelCase_:				str						=    torch.mean(torch.abs(snake_case_   )   )

							assert abs(result_sum.item() - 347.6357   ) < 1e-2
							assert abs(result_mean.item() - 0.4527   ) < 1e-3

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				int						=    self.scheduler_classes[0]
							UpperCamelCase_:				Optional[int]						=    self.get_scheduler_config()
							UpperCamelCase_:				Tuple						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Any						=    [39, 30, 12, 15, 0]

							with self.assertRaises(snake_case_    ,						msg="""`timesteps` must be in descending order."""   ):
											scheduler.set_timesteps(timesteps=snake_case_   )

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Union[str, Any]						=    self.scheduler_classes[0]
							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[str]						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Tuple						=    [39, 30, 12, 1, 0]
							UpperCamelCase_:				List[Any]						=    len(snake_case_   )

							with self.assertRaises(snake_case_    ,						msg="""Can only pass one of `num_inference_steps` or `timesteps`."""   ):
											scheduler.set_timesteps(num_inference_steps=snake_case_    ,						timesteps=snake_case_   )




			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				Any						=    self.scheduler_classes[0]
							UpperCamelCase_:				int						=    self.get_scheduler_config()
							UpperCamelCase_:				int						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				int						=    [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    snake_case_    ,						msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""    ,						):
											scheduler.set_timesteps(timesteps=snake_case_   )






